Enhancing an evolving tree-based text document visualization model with fuzzy c-means clustering

• Main Authors: Wui, Lee Chang, Kai, Meng Tay, Chee, Peng Lim
• Format: Conference or Workshop Item
• Language: English
• Published: 2013
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://ir.unimas.my/id/eprint/15839/1/Enhancing%20an%20evolving%20tree-based%20text%20document%20visualization%20model%20with%20fuzzy%20c-means%20clustering%20%28abstrak%29.pdf; http://ir.unimas.my/id/eprint/15839/; http://ieeexplore.ieee.org/document/6622363/
• Institution: Universiti Malaysia Sarawak

An improved evolving model, i.e., Evolving Tree (ETree) with Fuzzy c-Means (FCM), is proposed for undertaking text document visualization problems in this study. ETree forms a hierarchical tree structure in which nodes (i.e., trunks) are allowed to grow and split into child nodes (i.e., leaves), and each node represents a cluster of documents. However, ETree adopts a relatively simple approach to split its nodes. Thus, FCM is adopted as an alternative to perform node splitting in ETree. An experimental study using articles from a flagship conference of Universiti Malaysia Sarawak (UNIMAS), i.e., Engineering Conference (ENCON), is conducted. The experimental results are analyzed and discussed, and the outcome shows that the proposed ETree-FCM model is effective for undertaking text document clustering and visualization problems